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Free Tourism Dissertation: African Tourism Industry

Essay Topic: ,

A Strategic Analysis of the African Tourism Industry

Abstract

The study looks at the African Tourism Industry in terms of strategy for development over the coming decade, with particular reference to the differences between East and West African Tourism. A number of business models including SWOT and Porter’s Five Forces are applied to generate a strategic analysis and overall framework for implementation.A key aspect in the analysis is a small conceptual model using regression analysis to forecast the future evolution of the industry over the next 10 years.

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1. Introduction

1.1 Study Rationale

This study looks at the African Tourism Industry in terms of strategy for development over the coming decade, with reference to the differences between East and West African Tourism. A number of business models including SWOT and Porter’s Five Forces will be applied to generate a strategic analysis and overall framework for implementing strategy.A key aspect in this analysis will be a small conceptual model using regression analysis to forecast the future evolution of the industry over the next 10 years.

It is useful to investigate this area as the tourism industry is currently small in Africa, but has great potential in terms of development with a large number of unique attractions. In addition, regional tourism currently dominates, and the proportion of international visitors, who tend to spend more money, is low. With African countries starting to work together to develop cross-border tourism offerings, the potential for increasing visitor numbers and wider awareness of Africa as a tourism destination is enhanced(Euromonitor International 2010). Intra-African tourism is currently a strong growth area, although advertising focus remains fixed upon attracting non-African visitors (New African 2010). In addition, tourism offers the chance to increase economic growth of individual countries in the region.Currently a rate of just over 5% growth is forecast annually between 1995 and 2020.

1.2 Background to Study

There is good potential for growth in the African tourism industry. However, any growth in tourism needs to take into account the need for sustainability. Over the last 30 years there has been an increasing demand that tourism be ‘green’ and think about the environmental impact of its activities. Sustainability is a complex area covering a number of related but distinct areas. These include understanding the consequences of climate change, and how unmanaged tourism can contribute to these; the impact upon local economies and the local people of unchecked resort development; and the need to ensure resources such as energy and water are conserved. Indeed, sustainable tourism has been already suggested as a way to shape forecasting strategy for African Tourism.

In addition, tourism is, compared to other industries, less influenced by the ‘stakeholder’ model in which the interest of other parties are taken into account when making business decisions; profitability is still the main driver (Shitundu 2003).Given the need for incorporating sustainability, there is a strong argument for turning to a stakeholder perspective. This would turn the focus away from the demands of shareholders and profits to incorporate views from all other interested parties including local businesses, chambers of commerce and other organisations, local government, community organisations and residents. All who have a legitimate interest in the way tourism develops should be consulted equally, and this should be done at the same time as representatives from commercial organisations are consulted (Budruk 2010)

In order that African tourism can provide the best outcomes for both sustainability and for all stakeholders, it is vital that it is managed within a strategic framework.Such a framework can help to develop a perspective upon the way tourism can develop, but also provide a structure whereby the developed policy can be both evaluated and improved in the future (Dumont and Teller 2005). A number of different models for strategic frameworking exist. Certain models centre on problem identification and solving. Others look at the objectives of the organisation as central. In the case of Africa, an argument could be made for basing the strategic framework around problem solving, as a central issue concerns the best way to develop tourism in a sustainable way. On the other hand, if objectives of both organisation and stakeholders are taken into account, an objective based perspective might be used.

A strategic framework in this instance can be based upon a review of resources. This resource-based perspective allows the capacity of tourism in Africa to be seen in terms of valuable resources including physical locations, cultures and traditions. In this study a framework developed by Crouch and Ritchie (1999) will be used to shape strategic analysis. Their framework both sets out resources in broad categories and traces the relationships between them. In addition, Yoon (2002)’s development of the model allows the addition of stakeholders perspectives. One useful tool for quantifying the strategic framework is by developing a forecasting model to set out the evolution of the industry. Such a model can both maximise benefits and play down the negative impacts. Forecasts assume a continuity from past to present, and use data from the past to project into the future.

1.3 Dissertation Structure

The literature review will develop key ideas in more detail, including different theories of strategic development, and forecasting models, including limitations of such models, and justification for the model selected for this study. The current African tourism industry will also be discussed in depth, although there is relatively little study of the industry in the continent, and particularly few primary research studies. The literature review will also discuss the distinction between tourism in East and West Africa. East African perspectives emphasise sustainability, and it is gaining importance in West Africa as well. There are, however, key differences between the areas which will be elucidated. The literature review was developed on the basis of academic and industry electronic databases through key word searches.

Further sections will set out the hypotheses to be tested, which will emerge from the literature review. The methodology of the study will be set out including how data was gathered, sampling techniques, statistical modelling used and types of analysis carried out.

The results will be discussed in terms of the overall aims of the study.Any limitations of the study will be pointed out, implications for future study will be highlighted, and ways in which the study results can inform the African tourist industry will be set out.

1.4 Research Objectives / Questions

The study will address the question of ‘what is the best strategy for a coherent plan to develop tourism in Africa, taking into account the need for any such strategy to be sustainable?’. The difference between strategy for East and West Africa will be taken into account. The study aims to clarify best strategy by identifying key variables, which impact upon the likely future development of tourism in the region.

1.5 Summary

This section has given an overview of the area under investigation, the African tourist industry, which is currently underdeveloped and yet has potential to become a key destination. The structure of the following is set out, and the key research questions highlighted.

2. Literature Review

This chapter reviews previous literature and studies on the African tourism industry, the need for political, historical and social analysis of tourism and the continents accepted strengths and weaknesses. On a regional level the literature analysis of West Africa and East Africa will focus on how these regions have attempted to build on best practices related to tourism. The section will look to explore the theoretical background and context that justifies the main objective of the study, which investigates the foundations of establishing a strategic approach for forecasting the future evolution of the tourism industry. A key focus will be the acknowledgment of tourisms economic influence towards African country economies, as well as the importance of promoting sustainable tourism activities and attraction and the promotion of co-ordinated tourism development.

2.1. The Rationale for the African Tourism Industry Strategic Framework

A review of the literature agrees that the African continent has an enormous potential for tourism development (Medlik, 2002, Ankomah and Crompton, 1990). Further research articulated by Poon (1993) points to the “new tourism” and “global trend” towards “non-traditional destinations and long-haul travel.” This, he suggests illustrates “changing traveller wants in terms of destination experience,” which should provide a significant change in the places visited in Africa. However, as stated in the World Travel and Tourism Council, Africa seems to be under performing. Gauci et al (2002) explains this as a result of: “poor infrastructure, such as roads, electricity and water supplies; insufficient accommodation; unsatisfactory public health services; poor telecommunication facilities; and in a number of cases security problems.” Gauci et al also explains that difficulties with the installation of better management strategies, as well as the doggedness of actions which hinder competitiveness, have “contributed to [the] slow development of the tourism industry.”

Ankomah and Crompton observe that Africa’s population doubled to 700 million in the post-independence period, thereby placing tremendous stress on all aspects of economic, social, cultural, environmental, as well as political development. In this context Luvanga and Shitundu (2003) argue that: “rapid growth of the tourism sector is an important instrument of poverty alleviation, the creation of jobs, the sale of goods and services, support of cultural industries and source of foreign exchange.” It is significant to observe that the elevated status given to tourism by the United Nations (UN) and the Economic Commission of Africa (ECA), which clearly supports the potential role of tourism in the economic and social development of Africa (ECA, 1999). Indeed, research and literature highlight the increasing influence of the tourism industry in Africa and illustrate that, although there are many limitations, there remains reassuring indications for the state of tourism in Africa. For example, the Tourism Vision 2020 report given by the World Tourism Organisation (WTO) estimated that there would be an annual rise of 5.5 percent in international arrivals in Africa in the years 1995 to 2020. A similar rate was forecasted for intra-African tourism. However, the study by Luvanga and Shitundu (2003) showed the alternative side to tourism: “it is a complex industry often driven by the private sector to benefit international companies rather than local economies and causing environmental degradation.” These juxtaposing opinions have seen advocates of a strategic framework (Nelson, 2007, Heath, 2003) argue that: “as tourism develops and becomes intricate it will require strategic management of the process.” By developing a forecasting model to predict future developments, the sector should make the most of potential advantages, and restrict and divert the unconstructive effects that make sure the development conforms to national policy regulations.

2.2. The Purpose of the Strategic framework

Dumont and Teller (2005) argue that a strategic framework “will help to establish, evaluate and benchmark integrated tourism policy at the local level with a view to maximising the benefits of tourism on conservation and enhancement of heritage diversity.” This interpretation indicates a strategic framework aimed at fostering a pro-active approach, facilitating impact assessment and increasing awareness of sustainability issues for the future. The purpose here is to employ a strategic framework as a tool for forecasting the future in order for the tourism industry to be prepared for what might happen. Forecasting that is based on historical information and past events. Importantly, Fayol (1949) wrote that managing means looking ahead and that if foresight is not the whole of the management it is at least a major part of it. According to Fayol, “to foresee is to assess the future and make provisions for it […] plans need to have unity, continuity, flexibility and precision.” The organisation or industry must be run as if the future was foreseen. The plan of action is considered indispensable and that experience, from the past, was what determined the value of the plan. Fayol did, however, recognise that there would be unexpected events but the plan would serve as protection against such events and resulting enforced changes of course.

Predicting and preparing is, according to Ackoff (1983), the paradigm of management with predicting and forecasting being the more important. Forecasts are based on descriptions of the past and that data is fitted to a line and projected into the future. The assumption is that what has happen in the past will happen in the future. Thus, the general objective is directed at assessing the past to develop labour outputs and focusing resources and personnel to attain greater levels of performance and market competition.

At this stage of the paper it is vital to observe that there are diverse levels of strategy formulation, development and implementation, which correlate with the strategy’s objective. Alberts (2004) defines “three levels” of the strategic forecasting framework: the corporate level “where corporate goals are set, the target markets are defined and the terms and conditions of the corporate strategy are defined”; the “business unit level [….] [where] the business strategy level involves devising moves and approaches to compete successfully and to secure a competitive advantage over competitors”; the functional level, which includes “value analysis, business processes reacting to marketing, resources allocation and management and research and development.” Each level of the strategy looks to gain an edge in a market that is powered by market demands. Alberts says that this is “particularly necessary because tourism enterprises are exposed to a vibrant market where they need to survive through innovative techniques that will create a sustainable competitive advantage.” Innovative action is a main source of sustainable competition and can be established with well-structured strategies and systems.

2.3. Strategic Framework theories

There are a multitude of different business strategy formulation methods, models and theories. Smith (2001) suggests that: “the best way of formulating a strategic framework is for it to be derived from problem identification, meaning that the approaches should be problem based.” Elsewhere, Oldham, Creemers and Rebeck (2000) state that “the purpose and objectives of the enterprise [is] the foundation of the strategic formulation.” This model-orientated approach introduces a system which is based upon a flow chart system or a number of relational stages. Pazstor (2001) agrees with Hamel and Prahalad (1994), stressing: “different circumstances call for different types of strategy.” Mintzberg (1987) states that since the 1960s strategic frameworks “have had a clinical popularity with organisations” as it has gained a lost popularity, as it was unable to fulfil expectations and provide satisfactory results; namely generating money for businesses and their shareholders. Allaire and Firsirotu (1989) state: “this limited success is attributable not only to earlier poor practices but is also a function of ever rapidly increasing change of the business environment.” Significantly changing climates cause uncertainty and brings the suitability of strategic frameworks into question. Additionally, it is questioned “how to handle this ambiguity?”

The question arises why do industries need forecasting strategic frameworksThe literature suggests it is to reduce future uncertainty (Linneman and Kennell, 1977). Langley adds that part of the answer is to assist organisations make better strategies through a systematic logical approach. Loasby answers the question with three responses:

To understand the future implications of present decisions in order for the organisation to get the full benefits from its present decisions.
To understand the implications of future events in order to make decisions to prepare for the future. This is an attempt to forecast the future.
To prepare motivation and a mechanism for dealing with the above and reviewing assumptions about the future.

Relevant literature pays substantial attention to developing strategic with the purpose of dealing with such variables. Comprehending the various strategic perspectives is important as it permits the holistic understanding of strategy formulation and implementation.

2.4. Strategic framework: Analysing competitive industry structure

2.4.1. Porter’s competitive strategies

We now turn to review some papers covering the topic of Porter’s generic competitive strategies, the source for much business strategy analysis. In their study Caves and Porter (1977) generalize the theory of competitive barriers to entering an industry into a theory of mobility dynamics and decision-making behaviour of both emerging and going organisations. Porter (1979) establishes the link between competitive forces and competitive strategies. Porter (1980) presents the competitive forces and generic business competitive strategies for emerging, mature, declining and fragmented industries while considering entry and exit industry barriers. In his review of Porter’s generic competitive strategies Vanhove (2005) writes that when Porter’s two basic theories of competitive advantage, that is “lower cost” and “differentiation”, are adapted to the tourist sector. Lower cost is “the ability of a firm to produce a more comparable service than its competitors.” Differentiation is “the ability to provide unique and superior value.” How does this relate to forecasting in the tourist sectorImportantly, Treacy and Wiersema (1995) note that “competitive strategy is about two things: deciding where you want your business to go, and deciding how to get there.”

2.4.2 Resource based View (RBV)

Grant (2001) states: “recently there has been a resurgence of interest in the role of the firm’s resources as the foundation for firm strategy.” This is reiterated by Hampton (2003), Lawson (2003) and Kozal and Louisa (2006), who feel that this considers an enterprise’s capacity by “assessing the levels and the potential of the enterprise to improve within the ambits of available resources.”

Collins and Montgomery (1995) present five tests that define a valuable resource:

“Inimitability – how hard is it for competitors to copy the resource A company can stall imitation if the resource is (1) physically unique, (2) a consequence of path dependent development activities, (3) causally ambiguous (competitors don’t know what to imitate), or (4) a costly asset investment for a limited market, resulting in economic deterrence.”
“Durability – how quickly does the resource depreciate?”
“Appropriability – who captures the value that the resource creates: company, customers, distributors, suppliers, or employees?”
“Substitutability – can a unique resource be trumped by a different resource

Competitive Superiority – is the resource really better relative to competitors?”

How does the above relate to the tourism sectorMassukado-Nakatani and Teixeria (2009) epitomise the implementation of RBV in the examination of the tourism industry and explain that “[although] tourist resources are not explicitly illustrated as a resource category in RBV, they can be considered a physical (e.g. geographical location) or an organisational resource (e.g. local traditions and culture).” He identifies tourism resources as “the most important asset for tourism development because the resources are fundamental to any public policy that aims to improve tourism activities.”

The two above frameworks have combined to produce further research in the tourism literature:

Crouch and Ritchie (1999) established a complete and complex system for tourism destination management which built upon the theoretical concepts of “competitive” and “comparative” advantages (Porter, 1990; Enderwick, 1990). These asses a wide selection of “factor endowments: human resources, physical resources, knowledge resources, capital resources, infrastructure, and historical and cultural resources.” Yet it was disputed that listing the factors that influence the destination’s competitiveness in this framework is not suitable; but it is vital to comprehend their relationships. Conceptual models for destination competitiveness can be constructed from the factors: “competitive (micro) environment, global (macro) environment, core resources and attractors for primary elements of destination appeal, supporting factors and resources for secondary elements of destination appeal, destination management and qualifying determinants” (Go & Govers, 2000). Government and chance events are considered to affect competitiveness because of the effect they have over basic determinants. Bordas (1994) helped to identify Tourism Policy as an unrelated factor to the described strategy, and encouraged the theory that critical policy must be examined in greater depth. To do this, planning and development issues which contribute to destination competitiveness and sustainability must be considered (Ritchie & Crouch, 2000).

Yoon (2002) gave exclusive attention to the viewpoint of the tourism stakeholders’ and used this to theoretically construct a structural equation model of tourism destination competitiveness. This empirically tested the interaction of relationships of five particular constructs: “tourism development impacts, environmental attitudes, place attachment, development preferences about tourism attractions, and support for destination competitive strategy, where the first three are exogenous and the latter two are endogenous.” Tourism development impact creates new jobs and working opportunities, as well as encourages investment capital. Place attachment was found to be influential over stakeholders’ development of tourism attractions. This positively affected the support for destination competitive strategy.

Dwyer and Kim (2003) constructed a system of destination competitiveness that “enables comparisons between countries and between industries within the tourism sector.” Using the key factors of competitiveness studies, which were taken from Crouch and Ritchie (1999), the model recognises the demand conditions as an important “determinant of destination competitiveness” (Dwyer & Kim). This was not mentioned by Crouch and Ritchie.

2.4.3. Strategic forecasting framework

Since this study looks to examine the anticipation of a future within tourism, we must consider the question: “what are the literature viewpoints on forecasting theories?” Chandra and Menezes (2001) write that accurate forecasts for tourism demands are essential for the development of effective strategic plans. In this regards, Brignall and Ballantine (1996) note the availability of accurate tourism has important economic consequence for various organisations involved with tourism planning and the provision of tourism products and infrastructure. They further note that given the perishable of the tourism product, the need for accurate demand forecast is even greater. Chandra and Menezes identify that among the forecasting models using multivariate techniques, multiple regression is the most used and the relevant technique for forecasting international tourism demand

Further analysis of the literature reveals that empirical economic studies in tourism has looked primarily at four key sectors:

“The economic impact of domestic/or international tourism on a local economy” (Archer, 1977; Kottke, 1988; Zhou et al, 1997; Wang, 1977; Vaughan et. al., 2000 and Saayman et al, 2000).
“The economic importance of tourism for development” (Diamond, 1976; Piga, 2003; and Saayman et al, 2001).
“The economic impact of identified events” (Randall and Warf, 1996; and Grelan, 2003).
“Research efforts that are incorporating the explanation of tourism demand on international tourism flows” (Crouch, 1995; Coshall, 2000; and Smeral and Weber, 2000).

However, Prideaux et al (2003) observe that “given the frequent reliance of the former forecasting techniques” on previous experiences, which required explicit and tacit assumptions regarding the stability of relationships, “the ability of forecasting to generate long-term results and account for unforeseen events remains limited.” Prideaux et.al. observes that “short term forecasting may only factor in known relationships which observe trends.” Using this as the foundations for development, it provides an image of what may potentially happen should alterations arise along predictable lines. These are equilibrium and stability assumptions which are in contrast to “dynamic complexity and turbulent systems perspectives” (Laws et.al., 1998).

Many researchers (Witt and Song, 2001) recognise the boundaries of contemporary forecasting approaches, especially the problems that arise from the inability to foresee irregularities, for example drastic changes in consumer taste and demand. In order to remedy these shortfalls, researchers like Turner and Witt (2001) discovered that: “structured time series models incorporating explanatory variables produced the most accurate forecasts.” Observing relevant non-economic variables is disadvantageous to development in the future, as well as to the fluidity of their significance; this offers a great amount of problems for the forecasters. Uysal and Crompton (1985), for example, noted that: “there are a number of limitations confronting demand forecasting: ignoring supply factors, the omission of non-economic factors which may have long-term consequences and the appropriateness of variables to change.” In addition, Prideaux (2003) explains that to these variables, a selection of other non-specific crises and disasters, including “domestic and international economy and natural disasters such earthquakes, cyclones or hurricane” must be contributed. Forecasters such as Witt and song (2001) attempt to comprehend these scenarios by utilising dummy variables which accommodate the impact of “one-off” disasters such as the 1970s “oil crises.” Irregular and ambiguous obstacles remain constant challenges to contemporary forecasting.

Witt and Song (2001) agree that “a more sophisticated approach utilising time varying parameters (TVP) regression to model structural change is one solution to the problem of predictive failure encountered by causal tourism demand for forecasting models.” They express that, although TVP strategy is able to imitate a variety of shocks and could affect the association between explanatory variables and dependent variables, TVP assumes that explanatory variables are exogenous. Witt and Song (2001) further notes that: “where there is some doubt about the creditability of the latter assumption the vector autogressive (VAR) modelling approach may be more appropriate.” This is because in the VAR model every variable is treated as endogenous.

Acknowledging the limitations of contemporary forecasting theory to manage the unforeseen, Faulkner and Russell (2000) raise an alternative theory, stating that because of the “uncertainty of the unexpected, authorities need to implement policies for coping with the unexpected disruptions to tourism flows”.

A well-developed literature typified by Sonmez and Graete (1998); Lepp and Gibson (2003); Ritchie (2004); Gunn, (2002); and Inskeep (1991) recognise that there exists a great variety of events which exist outside of the research of predictions, that standard forecasting techniques can be expected to yield. One the other hand, Prideaux (2003) notes that: “tourism literature has not begun to investigate the rich range of techniques developed in the risk management literature.” However, this could potentially surrender models, frameworks and theories which could aid tourism forecasters and planners, and help them to manage unforeseen disasters and events.

This, therefore, raises the question: where does this leave the study of forecasting within the tourism industryFaulkner (2001) notes that if change is slow and ordered, predictable forecasting “may yield a high degree of accuracy. On the other hand, where events follow the normal course of history and exhibit a tendency to sudden, large-scale instability and unpredictability, forecasting loses its potency and an alternative form of prediction is required.”

2.5. Background: A Conceptual Framework

Conceptual frameworks and theory are “typically based on combining previous literature, common sense and experience” (Eisenhardt, 1989). A look at the literature reveals a tendency towards sustainable tourism as a forecasting strategy for African tourism. The theory of sustainable development is described as “the central challenge of our times” (Wheeler, 2002) and “the issue of the twenty-first century” (Harrison, 2000). Jabareen (2004), even goes so far as to describe it as “one of the pervasive icons of modernity.” Yet, despite the attention it receives, the implementation of sustainable development in practice has been extremely poor given the continued decline of environmental quality measures on a global scale (Millennium Ecosystem Assessment, 2005). Numerous reasons can be put forward for this situation including vagueness of the term (Mayumi and Gowgy, 2001) and disagreement over what should be sustained (Sachs and warner, 1997). In an effort to clearly define the indicator selection process, attempts to construct frameworks were made, arranging the development and selection process. Indicator sets and monitoring frameworks are constructed from “indicator/measures” which are chosen an ad hoc manner (Waldron and Williams, 2003). White et al. explain that: “a conceptual framework allows for the coherent and consistent selection of indicators.” Therefore, it can be seen that the indicator selection process is value laden. It is left to be considered: should the stakeholder opinion alter over the importance attached to various definitions of a “good indicator” such as: assuming the trade-off between cost and complexity; the very objectives chosen; and the baseline and the benchmark data. Therefore, the explicit strategy framework permits a “transparent, responsive and robust process for indicator selection.”

2.6. African Tourism Industry

Naude and Saayman (2004) identify that “the economic dimensions of tourism to Africa, and specifically the determinants of the demand for Africa as a tourist destination are neglected in the economic research literature.” Lim (1997) “looked at over 70 studies of international tourism demand, although these did not look in any extensive detail at African nations. Eilat and Einav (2003) argue that this is a flaw in contemporary international empirical literature on tourism demand”: the deficiency of “rigorous panel data analysis.” The deficiency of suitable empirical studies on tourism to Africa has contributed towards the “limited policy guidance” to the sector, as stated by Christie and Crompton (2001).

Naude and Saayman (2004) further go on to explain that “so far most research on tourism demand and international flow of tourism have focused mainly on explaining tourism demand and flows in developed countries, with little reference to developing countries and even less to explaining tourism in Africa.” This discovered that literature tends to pay more attention towards the affect of the exchange rate and income on tourism receipts, and does not look to explain “country-specific determinants” of tourism arrivals

2.6.1. Determinants and obstacles to tourism growth in Africa

At this point in the paper it is important to ask: “Why do different nations invite greater levels of tourism than others?” It is a question that has been asked by various researchers of the tourism industry, and has been used as the basis for a wide variety of studies since the 1970s. Crouch (1994) explains that: “responsiveness of demand for international travel varies, depending upon the nationality of the tourist and the specific destination involved.” It can be seen, therefore, that “demand-elasticity for international tourism” alters “depending on the country-of-origin and country-of-destination.” Crouch (1995) concludes that “the demand for tourism is a function of the tourist’s country of origin, since cultural differences affect travel behaviour.”

Coshall (2000) indicates that: “there are many financial, perceptual. Cultural, social and environmental factors that could be used to try and explain international tourism flows.” The independent study that generated the information on which these findings are founded was compiled from looking at the tourism demand in first world countries, with only small reference given to developing nations. Kester, (2003) and Gauci et.al. (2002) argue that certain factors not included in previous studies need to be identified. For example, Christie and Crompton (2003) put forward the view that the greatest obstacle to Africa’s tourism sector growth “is its lack of price and quality competitiveness.” Kester argues the view that the major obstacles to tourism arrivals in Africa are “insufficient air transport, a deficiency in facilities and accommodation, lack of image and poor perceptions, poverty, disease and conflict.” Gauci et.al. (2003) discuss the problems facing tourism in these areas, such as underdeveloped public health services or fears for personal safety. Eilat and Einav (2003) find that “political risk has a significant impact on tourism demand in both developed and developing countries.”

Naude and Saayman (2003) make the identification that: “given the challenges facing Africa and the need for sound policy advice for promoting tourism, it seems more appropriate to identify the long-run determinants of tourist arrivals.” Naude and Saayman note that the uses of fixed effects estimator “allows the pick up of short-term effects since it focuses on time series components of data.” Naude and Saayman (2003) used “cross-section data and panel data for the period 1996–2000 to identify the determinants of tourism arrivals in 43 African countries, taking into account tourists’ country of origin.” The findings greatly indicate that “political stability, tourism infrastructure, marketing and information, and the level of development at the destination” are key determinants of travel to Africa. Typical “developed country determinants” of tourism demand, for example the amount of income within the origin nation, the cost of travel, are not as important in comprehending and clarifying the demand for Africa as a tourism destination. It is advised that “attention should be given to improving the overall stability of the continent and the availability and quantity of tourism infrastructure.”

The review of the literature on forecasting analysis suggests that any future strategic framework must include the above factors to gain substantial weight when the aim is to develop relevant forecasting models in the African context.

2.6.2. East African tourism

Much of the strategic framework in the literature for east Africa tends to encapsulate sustainable development based on conservation. For example, this was the purpose of Nelson’s (2007) study on strategic frameworks for east Africa, which covered the countries of Kenya, Tanzania and Mozambique. The analysis was to create a basis for development and promoting forms of tourism that contribute to biodiversity/conservation in eastern Africa in line with the World Wide Fund for Nature’s (WWF) global mission and objectives. Another study on eastern Africa by Mugo (2006) also focused on the strategic framework for conservation. The study used situation analysis to devise a strategic framework to link related initiatives that were being undertaken in the region by national governments and international agencies. This included the Eastern African Ecoregion strategic framework, which focuses on coastal and marine conservation issues.

What about the application of regression based strategic frameworks in the East African literatureAn example is the study by Summary (1987), which looked at tourism in Kenya between 1963 and 1982. The study concentrated on the variable that tourism was one of the top three domestic exports during the period and aimed to influence tourism demand and policy makers in planning growth strategies. The results of the study indicated multivariable regression analysis has limited usefulness in identifying the significant factors which influence tourist’s decisions. Summary notes that data problems and multicollinearity caused unsuitable results in one case, while model specification appears to be a problem in another. The author concludes that quantitative studies should be supplemented by quantitative analysis in order to for Kenyan policy makers to make optimal decisions.

2.6.3. West African Tourism

Ige and Odulara (2008) write that the “increasing importance of sustainable tourism has become imperative to West Africa as a regional economic community.” A review of the literature focuses on the factors that explain growth. For example, models developed by Barro and Sal-I-Martin (1991) and Mankiw, Romer, Weil (1992) introduces “the concept of conditional convergence” and permit the analyst to consider the various nuances of different nations, for example the level and development of technology. Ige and odulara (2008) note that “most of the empirical studies have used a cross-sectional analysis, although with a growing availability of panel data, and the development of econometric techniques has been used widely to prove hypothesises.” A review of the literature brings up a study by Ige and Odulara (2008) which examined the influence of tourism on the West African economy by utilising pooled data on ten West African nations in the years 2000 to 2004. Studies showed that tourism certainly is influential in West Africa. This could be explained as the tourism destinations in West African economies are usually located within the “commercial nerve centres” which greatly affect the economic prosperity and, therefore, explaining the reasons for the regression. The findings also indicate that the influence of tourism must not be overlooked within climates of sustainable management of tourism to attain the maximum advantage of topical relevance to West African macro economic performance. This means that the economic performance in West Africa may be improved by proper tourism development policies which encourage openness with a lot of importance placed upon the liberalisation policy. The results of the model showed that for West African nations, the development of the tourism industry has seen greater economic development during the period 2000 to 2004. Thus, Ige and Odulara (2008) conclude that “West Africa needs to strategically harness its tourism potential in order to improve its economic performance.”

From the research it is important to note strategic frameworks are essential in developing tourism forecasting models but as stated by Crouch (2007) “destinations vary enormously and countries compete for different market segments in tourism, and so it is more meaningful to compare countries by market segment.” Indeed, it can be considered that the elements which may have a significant influence within one particular segment may be less significant in another.

3. Case Study – Methodology

This section will set out the means by which the case study will be conducted. First, the models that will be used to analyse the tourism industry in Africa will be explained. Subsequently, this section will look at the methodology for the regression analysis.

The models which will be used in the case study fall into two broad categories. On the one hand, some models help define what the overall strategic framework for the African tourism industry might be, on the other, further models help formulate the best plans in more specific terms.

Models to help formulate an overall strategic framework include Smith’s (2001) problem identification theory, Oldham, Creemers and Rebeck’s (2000) model based on organisational objectives, and Hamel and Prahalad’s (1994) contingency view of matching model to circumstance. In addition, other models such as a simple SWOT analysis or PESTEL overview can help link strategy to circumstance

Models which help add detail to the framework include Yoon’s (2001;2002) ‘Structural Equation Model’ and the similar models developed first by Crouch and Ritchie (1999) and later by Dwyer and Kim (2003) based around destination competitiveness and a hierarchy of priorities.

This section of the dissertation will look in more detail at the models which will be used in the case study, briefly outlining their theory and making clear how they work.

3.1 Overall Strategic Framework Models

This section outlines models which can help formulate overall strategic frameworks, and which will be used in the case study of Africa, below. The section will look at the notion of Butler’s lifecycle planning and ‘destination visioning’. Strategic planning needs to incorporate a long term perspective, the development of a holistic, integrated plan to manage change through goal formation and also formalise a decision process around the distribution of destination resources. Such a plan should also allow quick responses to changing situations. Kotler et al (Cooper 2002) have been influential in helping shape this overview of what such planning must incorporate. Strategic planning is particularly important for sustainability, as goal setting allows all stakeholders to have input into the future of the destination and help create a clear shared vision. There are, however, problems, for example the views of different shareholders with different value systems might be difficult to reconcile (Cooper 2002).

The ‘Life Cycle’ approach offers a technique for destination management strategy and a way to incorporate a long-term perspective. By differentiating between different stages in the life of a destination, management approaches can be tailored to these stages. The notion was developed by Butler (1980), who suggested that destinations cycle through six sequential stages: exploration, involvement, consolidation, stagnation and decline / rejuvenation (see figure 1) (Dong et al 2004).

Figure 1: Destination cycles through six sequential stages. Source: Butler (1980)

StageTourist CharacteristicsLocal consequences
ExplorationVisitors explorers, travel individually, irregular patterns, predominant attraction naturalLocals do not understand needs of visitors
InvolvementStart of variation in tourist numbers, low/high season. Man made facilities appearResidents start to dedicate resources to visitors, some advertising
ConsolidationVisitor numbers reach plateau. Package tours.Local economy dependent upon tourism.
StagnationDestination well established but loses fashion. Peak capacity reached. Tourists psychocentricLocal economy dependent on tourism
DeclineSome destinations decline – decrease in market…Impact on local economy as visitors decline
Rejvenation… others recover by changing attractions, new natural resourcesFurther pressure on local economy

It is possible to adapt the idea of the life cycle to integrate sustainable tourism with appropriate management strategies at each stage of the cycle with holistic planning (Bramwell and Lane 1993). One useful approach is ‘Life Cycle Analysis’ (Jain 1985) which combines the notion of the life cycle with Porter’s competitive position (dominant to weak). This is set out in figure 2 (Cooper 2003).

Figure 2: Jain’s Life Cycle Matrix (adapted from Cooper, 2003)

Competitive PositionStages of Industry Maturity

EmbryonicGrowthMatureAging
DominantFast growing

Start upFast growing, leadership

Renewing

Defending positionDefend position, Renew, cost leadershipDefend position

Focus

Renew

AdaptStrongStart up

Differentiate

GrowthFast growth

Catch-up

DifferentiateAttain cost leadership

Renew

Focus

Change with industryFind and retain niche

Grow with industry

HarvestFavourableStart up

Differentiate

Focus

GrowDifferentiate

Focus

Grow with industryFind and hold niche

Renew

Turnaround

Differentiate

Grow with industryRetrench

TurnaroundTenableStart up

Grow with Industry

FocusHarvest, Catch-up

Find niche

Hold niche

Focus

Grow with industryHarvest

Turnaround

RetrenchDivest

RetrenchWeakFind niche

Catch up

Grow with industryTurnaround

RetrenchWithdraw

DivestWithdraw

Another useful approach is that of ‘Destination Visioning’. This was suggested by Ritchie (1994) as a way to address the needs of strategic planning for tourism. This approach places power in the hands of the community, including local government, residents and businesses who have a central role in creating a strategic plan for the destination. There are three key ideas involved in Ritchie’s destination visioning. First, the vision needs to bring together the views the entire community as well as other stakeholders. Second, all involved parties need to agree about the vision, and third, the vision needs to incorporate long-term development plans. Cooper (2002) elaborates a practical strategy for delivering this vision with firstly a ‘destination audit’ – the commissioning of research to look at the nature of tourism in the region currently, the second stage ‘position stagements’ for key areas including market, investment, environment, and followed by ‘visioning workshops’ – perhaps the most important element with workshops held around the area to find out the views of all community members about tourism in the area. This feeds into the next stage ‘Development of the Vision’ where results are analysed and used to prepare a development plan. Finally, this is followed by the implementation scale. While there are acknowledged difficulties with Destination visioning – for example problems in making sure all community views are gathered, and difficulties gaining agreement on some areas, it seems a useful tool for developing a sustainable tourism plan (Cooper, 2002).

The case study will also bear in mind Oldham, Creemers and Rebeck’s (2000) model based in purpose and objective, and the more contingent approach championed by both Pazstor (2001) and Hamel and Prahalad (1994).

While there has been much discussion regarding whether strategic frameworks are a useful tool for developing organizations and ventures, perhaps due to the rapid change in the business environment, it is assumed in this study that they can add value and help formulate a better plan to deal with the future. They will be used in the case study to provide an overview for the tourism industry in Africa.

3.2 Models to Add Depth and Detail

This section sets out further models which will be used to add detail and depth to the case study by helping flesh out the overall strategic framework for African Tourism as it faces the next 10 years. Models of micro and macro environments can be useful, as are resource based views. A model by Yoon, and one based on ideas from Porter, developed by Crouch and Ritchie’s (1999) and Dwyer and Kim (2003) are also discussed.

Many useful models look at the macro and micro environments. The macro environment equates to the external environment and involves the identification of threats and opportunities to the enterprise. Tools such as PESTEL (which looks at Political, Economic, Social, Technical, Environmental and Legal issues) or STEEP (Socio-demographic, Technological, Economic, Environmental and Political influences) are useful here. Other approaches extend these analyses by including ‘international’ ‘communications’ and ‘infrastructure’ for example. The micro environment, on the other hand, looks at the immediate competitive threats to the enterprise. Here Porter’s ‘five force’ model to understand competitive position (see figure 4) is useful (The Hospitality Leisure Sport and Tourism Network 2011 online).

Figure 3: Porter’s Five Force Model

Porter’s model is based upon an economic model called ‘Structure-Conduct-Performance’ (SCP), which assumes that the structure of an organization and the industry in which it operates dictates how that organization behaves, and in turn this determines profit (performance) (Henry 2008). The model helps an organization or enterprise determine the merits of any course of action by looking at the way the five forces Porter identifies are interacting. While Porter developed the model from the point of view of organizations already operating in an area, it is also valuable for organizations or enterprises determining whether to enter a competitive environment (Henry 2008).

Another useful approach is to look at organisational resources and competencies. The ‘Resource Based View (RBV) looks in detail at the internal resources of the enterprise to work out how these can be used to gain maximum advantage. Porter’s value chain EXPLAIN concept can be used to understand these core competencies (The Hospitality, Leisure, Sport and Tourism Network 2011 [online])

Yoon’s ‘Structural Equation Model’ (2001) concerns the perspective of stakeholders in the tourism enterprise. It sets out the relationship between five areas: tourism development impacts, environmental attitudes, place attachment, development preferences about tourism attractions, and support for destination competitive strategy. The first three are exogenous, the latter two endogenous. Residents support for any future tourism, in the model, is determined by the way they perceive various aspects of tourism. Each of four elements or dimensions influences the total tourism impact, which in turn impacts upon the support for future tourism development. Yoon’s model is based in social exchange theory, which suggests that people are more likely to take part in an exchange if they think they will benefit from the exchange and will not occur too many costs. Residents need to perceive the benefits of tourism outweighing the disadvantages in order that they give their support to future developments. The model is set out in fig 4 (Yoon et al 2001).

Crouch and Ritchie (1999) develop a model based on idea of competitive and comparative advantages, including human, physical and knowledge resources, capital, infrastructure, historical and cultural resources. In this model, ‘attractions’ are the basic building blocks of a destinations appeal to the public, and act as key motivators for visits. They can include cultural and natural elements. The model moves beyond merely listing advantages to incorporate a way to understand the relationships between the factors in a ‘Conceptual Model of Destination’ which looks at the micro environment (the competitive situation), the macro (global) environment, core resources and attractors for primary destination appeal elements, supporting or secondary destination appeal elements and also qualifying determinants.

Dwyer and Kim develop a model, strongly influenced by Crouch and Ritchie (Kozak and Andreu 2006), based around destination competitiveness that allows comparisons to be made between countries. They base competitiveness between destinations in terms of the various characteristics of a destination which make it desirable to visit. They also suggest that these factors can be managed in a process of ‘Destination Management’, promoting the appeal of core resources, strengthening their quality and adapting to contingent conditions (Dwyer and Kim 2003). Tourist destination attractiveness include natural resources (scenery, parks etc) and artificial resources (museums, hotels, culture). Administrative factors should increase attractiveness of basic resources and amplify their appeal. Administration should be conducted efficiently and with adaptation to contingencies (Navickas and Malakauskaite 2009). Factors form a hierarchy, with natural resources the base of a pyramid, followed by created resources, then administration. Above these levels is the need for a cohesive policy and development. This pyramid will be used to structure the case study discussion. The similarities between the two models are drawn out in figure 5:

Figure 5: Dwyer and Kim, Crouch Ritchie Models (adapted from Dwyer and Kim 2003)

Dwyer and Kim (‘Integrated Model’)Crouch-Ritchie Model
Natural Resources

Cultural / Heritage ResourcesCore Resources (Climate, Culture, Activities Mix, Special Events, Entertainment etc)Supporting Factors and Resources (General Infrastructure, quality of service, accessibility of destination, hospitality)Supporting Factors and Resources (Infrastructure, Accessibility, Hospitality, Enterprise)Destination ManagementDestination ManagementSituational conditionsDestination Policy, Planning, DevelopmentCompetitive (micro) environmentGlobal (macro) environmentDemand ConditionsQualifying and Amplifying Determinants

3.3 Regression Analysis

In addition to the tools outlined above which will be used to inform the case study, this study will also include data interrogation. Data will be collected from Africa as a whole and East and West Africa as sub regions to determine the change over time for key variables upon tourism. A regression analysis will also be included on the data. Regression analysis is a statistical technique used to predict the value of one variable when we know the values of other variables. It models the relationship between two or more variables (Cohen 2007). Simple linear regression helps identify the most representative straight line connecting two sets of variables, which multiple regression maps the relationships between more than two variables. The latter will be used in this case. (Buglear 2004).

3.4 Section Summary

This section has examined the methodology to be used in this study. Tools and models for strategic planning were discussed, as well as additional models which can be used to add depth. To summarize the tools to be used, Butler’s (1980) lifecycle planning allows a long-term perspective on African tourism to be taken, a perspective which is currently missing. By combining this with Porter’s competitive positioning, Jain’s (1985) model suggests how this strategic position can be combined with an awareness of the rest of the tourism market. Ritchie’s destination visioning can also inform strategy by allowing all stakeholders to have a say in how tourism should develop in their area. In addition to tools which help develop a wide-reaching perspective, a number of tools for detailed analysis are useful. These include PESTEL, which allows key factors in the market environment to be isolated, and Porter’s ‘Five Forces’, which provides a way of seeing the industry in terms of competitive position. Dwyer and Kim (2003), and Crouch and Ritchie (1999), also suggest a useful model specific to the tourist industry. Finally, regression methodology was looked at.

4. Case Study: African Tourism

4.1 Overview of Africa and Tourism using Business Models and Tools

The methodology has set out a number of useful tools for analysing the resources of Africa as a tourist destination, which can be used in turn to develop an overall strategy for tourism, both in Africa overall and with references to differences between East and West. The following will discuss Africa in these terms, first using tools identified in the literature review such as PESTEL, STEEP and Porter’s Five Forces to look at Africa’s current position, and then taking a wider strategic view, again drawing upon tools and models discussed in the methodology.While tools such as PESTEL and STEEP distinguish different areas of consideration, to some extent these divisions are artificial, and the areas overlap to some extent.

4.1.1 The Political Situation

Most available information relates to the political and economic climate in Africa, and what it means for tourism. Tourists are, for example, highly sensitive to political instability, and can fear for their personal safety. It has been suggested (Okech 2010) that only democratic countries with a respect for law and human rights can create the stability which is necessary for tourism development.

The political history of Africa is complex, with many countries facing severe political problems which have their roots in colonialism and its aftermath. The Cold War and, more recently, Globalisation, have also had an impact. However, international news coverage can lead to a skewed notion that Africa is a state of ongoing political crisis. In fact, most of the countries which make up Africa, despite problems, are not in meltdown. In addition, the 1990’s saw a movement dubbed ‘Africa’s Second Liberation’ or ‘Second Independence’ with more than 20 countries moving from authoritarian regimes to more democratic decision making. To some extent however, countries are still marked by (Exploring Africa 2011 [online]) lack of democracy and plagued by rivalries between ethnic, religious and regional groups. Human rights abuses, corruption and authoritarian regimes still exist.This can prove a disincentive to more main-stream tourists.

Despite these problems, many African governments are aware of the potential of tourism. Tourism allows governments to profit financially as they gain both through taxes and indirectly through duties upon items tourists buy including drink, petrol and hotel accommodation. To this the income from foreign exchanges and tax on those employed in the tourism sector can be added (Okech 2010). Countries are consequently investing heavily in tourism development, attempting both to promote their countries and to redeem the image of the destination. For example, Nigeria’s Federal Capital Territory have allocated large resources to tourism (Kareen 2008).

This new focus on tourism has been further fuelled by international development agencies such as the World Bank, the International Finance Corporation, the British Department for International Development and the SNV Netherlands Development Organization. However, investment from outside needs to be matched by government policy in order that investment can contribute to economic and social development in the most ‘joined-up’ way.Cross–border initiatives are also increasingly important, as tourists frequently travel across a number of African countries during their stay. The ‘New Partnership for Africa’s Development (NPAD [online] 2010), for example, sees a number of African companies join together with a shared recognition that tourism has great potential for economic development. Throughthe ‘Tourism Action Plan’ the NPAD set out a strategy for managing this potential. The strategy encompasses including key objectives such as creating a regulatory environment, strengthening planning, improving marketing and communications, promoting research and development, formulating education and skills training, and improvements to infrastructure (Rogerson 2007).

Many individual countries have a range of strategies to boost tourism. Some offer incentives; for example Tanzania has reduced visa costs. Some governments develop incentives for industry by offering, for example, help with marketing cash subsidies, business finance or skills development. Lack of funding is always an issue especially in countries like Africa where there are high levels of poverty, and tourism might seem less of an immediate priority.

In addition to initiatives by individual countries, there is a move towards establishing links between African countries to help tourism, as visitors often want to see more than one country. An example is a recent links between Angola and Nambia, another the ‘Peace Parks’ – trans-frontier conservation areas, parks which cross boundaries and which need joint management by governments. The Peace Park foundation was created 1997 and there are now 10 established parks. Governments are learning from more established destinations, for example South Africa (Euromonitor 2010)

However, it is also recognised that governments need to take pro-active approach which takes into account input from all stakeholders, and that there is a need to draft policies and through consultation with all residents. There is an equal need for planning control, investment incentives in order to include even the poorest areas in initiatives (Okech 2010). However, while this aim is clearly desirable, it has to be questioned whether African countries will be able to implement this in practice, given some history of less than fair business practices and the existence of bribery and corruption in the past. This is an under-researched area where more primary research would be welcomed.

Overall, Africa’s political situation has meant it has been at a disadvantage in tourism terms in previous years. Not only are countries hampered by undemocratic governments and have to deal with challenges such as poverty and disease which mean there is less money to boost tourism, but Africa’s difficulties mean that it can be avoided by travelers who assume it is too unstable and poverty-stricken to be a good holiday destination. However, there are signs that governments are recognizing the potential of tourism to improve Africa’s finances, and also working across country boundaries to strengthen their approach.

4.1.2 Economic Aspects

In terms of the economy, Africa overall has acknowledged problems including economic stagnation, international debts, deficits, rising inflation and lack of growth (Rogerson 2007).There are some signs that the economy is slowly improving, especially in terms of international trading relations, and particularly relationships with China and India. For example, Africa-China trade was 10.6 billion dollars in 2000, 40 billion in 2005 and rose to 107 billion in 2007. Already over 700 Chinese companies operate in sub-Saharan Africa. China has also been involved in the development of Infrastructure including roads and other transport links. Oil producing regions in Africa, for example Sudan, Nigeria and Angloa, are growing in international importance (Euromonitor 2010). International investment has doubled in size between 2004 and 2005 due largely to the trend for China and other Asian countries to increase their presence and second the improvements to African infrastructure generally and particularly to the financial infrastructure including expansions of the debt and equity markets (Nelson 2007). In addition, Africa seems to escape the worst of the international recession: Africa as a whole has shown higher GDP growth than the global average, with a slight rise in average spend. However, the recession still had an impact due to a decline in visitors from regions hit by downturn more severely. Despite these favorable signs for the future, the African economy has declined in most countries over last few years with lower standards of living and higher levels of poverty. Naturally related problems including drought and famine play a part; in addition political factors contribute to this less than favourable outlook: for example Kenya suffered a decline after political violence in 2007/8 (Euromonitor 2010). There has been some increase in poverty levels overall, and falls to standards of living (Okech 2010). There exist wide diversities between the different African countries in terms of Gross Domestic Product (Kareen 2008)

Against this background, there is widespread hope that tourism offers a way to boost economy (Rogerson 2007). Where tourism infrastructure does currently exist, it is often foreign-owned. There is evidence to suggest that this hope is well-founded: some countries in Africa, for example The Gambia and Ethiopia, have experienced 20% growth in tourism over the last 20 years. Rates of increase are different in different regions, but the trend is towards growth. Overall, over the same time period, Africa has been increasing its market share of the tourism industry with 60% of international tourists now visiting for leisure purposes. In 2005 Africa had the best performance for growth of international arrivals of all the world tourism organisation UNWTO’s areas. Tourism offers opportunities to all, as the market is growing, and has tripled between 1970 and 2003 with increases set to continue (Nelson 2007). Tourism offers particular opportunities to Africa as it is relatively poor in exportable commodities. This is confirmed by existing research. While there is a lack of published studies in the area, those that do exist back up the idea that tourism can work for Africa. For example, Fayissa, Nsiah and Tadasse (2007) – found that tourism has contributed to the GDP and economic growth of African countries, and recommended strengthening the tourism industry for economic advantage. Other researchers writing about the benefits of tourism wider afield suggest that tourism is beneficial for economic growth particularly for developing (rather than developed) (Eugenio Martin et al 2004). Other researchers found tourism played a positive role for the economy by increasing competition amongst providers of tourism services Krueger, 1980). In 2008, Kareen found, through analysis of panel data for 36 African countries, that tourism and economic growth are significantly related. He also suggests that tourism as an export product can be used to predict future economic growth in Africa. In addition, he suggests that there is a two-way relationship between tourism expenditure and economic growth with one feeding into the other. Higher tourism expenditure leads to higher growth, and accelerated economic growth in turn leads to more tourism. He concludes that this relationship needs to be more widely recognized and integrated into strategy (Kareem 2008). Kareem’s study is a welcome addition to an area which currently lacks research. However, it is primarily concerned with statistical analyses of panel data, and less with discussing the implications for promoting tourism in Africa. More discussion would be welcome to clarify what his findings mean for the industry as a whole.

The negative economic impact of tourism also needs to be kept in mind. The bulk of purchases made by tourists are non-exportable. By consuming produce of interest to the local market, tourism can make these scarcer and more expensive for local people (Kareen 2008). Mass tourism can also have a negative impact on sustainability and the environment, which will be discussed later.

One particularly important area of the economy and the impact of tourism is in the area of employment. Tourism is labour intensive, and creates a large amount of jobs including guides, interpreters, positions in travel, hotel vacancies, catering and entertainment, cultural and sports jobs. In addition it boost a number of jobs in the informal economy including prostitution and drugs.Currently, tourism provides between 2 and 6% of jobs in Africa, with women representing 50% of the workforce.While tourism offers the potential for increased employment, there are a number of problems to be negotiated. Current employment opportunities tend to be low or unskilled, and the infrastructure is lacking with little job security, little formal training or employee development, and few prospects for career development or personal improvement. Factors such as these cause a demoralised workforce and can impact upon productivity. In addition employment is seasonal with most travel taking place in the northern hemisphere Winter, and with a quieter period between April to August. This particularly effects beach destinations including Kenya in East Africa and Gambia in the West. Many employees lose their job in low season. A further problem is that the concept of tourism is not universal. Many people in Africa, especially those in the more remote villages, do not understand the idea, and therefore fail to see the opportunities for employment and economic enhancement (Kareem 2008).

Economic considerations cannot be seen in isolation however. It should be noted that poverty, which is rife in Africa, is not just about income. It forms a complex two-way relationship with disease, literacy, the environment, education, access to justice, disempowerment and infant death (Okech 2010)

4.1.3. Other Factors

While politics and economics are perhaps the most important factors to consider in devising a tourist policy for Africa, other factors play a part. One currently important socio-economic factor is the growth of interest in and demand for eco-travel, sustainability and ‘pro-poor’ tourism. Interest in these areas have been worldwide, as people have become increasingly aware of the consequences of mass market tourism. While it can bring economic advantage to tourist destinations, there are also many negative consequences including damage to the region environmentally, displacement of people, cultural upheaval, and (through foreign ownership) funds not benefiting local people. The original focus of sustainable tourism was upon protecting the environment, for example native species and bio-diversity were damaged by construction of hotels, roads and similar, but this focus has widened. The remit now includes social, economic and cultural facets, and encompasses varied areas including the ‘greening’ of the industry by a new focus upon waste management and energy efficiency, protection of all resources from the environment to local cultures, the awareness of the importance of involving local communities in initiatives, and ‘pro-poor’ measures (Kandari and Chandra 2004).

Africa’s environment is one of the key attractions for visitors, as it has many areas of natural beauty and interest (Spenceley 2008). Key natural attractions include Victoria Falls in Zimbabwe, Okavango Delta in Botswana and the Namib Desert in Namibia (Bennett et al 2001). However, there are other issues which impact upon these natural attractions, and which make incorporating a sustainable perspective into tourism strategy imperative. Parts of Africa are subject to severe climatic conditions, and the natural attractions are also threatened by human action, for example the destruction of the rain forest and savanna, and changes to the levels of bio-diversity amongst plants and animals. These environmental issues have led to political and cultural changes, for example as early as 1977 Gambia formulated the Banjal Declaration as a response to loss of wildlife. This aimed to protect biodiversity, conserve existing resources and ensure that species do not become extinct (Weaver 2001)

Despite the relatively small size of the tourism industry in Africa currently, there has been widespread recognition of the need to promote sustainable development in the industry. The World Bank, for example, is committed to sustainable management in Africa in order to ‘Enhance Livelihoods’, ‘Protect People’s Health’ and ‘Reduce People’s Vulnerability’ to environmental risks. The African Region Environmental Strategy (ARES) also makes the support of environmentally oriented tourism a priority (World Bank 2001)

Pro-Poor tourism is a fairly recent concept, which aims to ensure that revenue flows back go grass roots levels and entrepreneurs (Kareem 2008). Pro-poor tourism is an initiative which hopes to increase benefits to poor locals from tourism, and tries to integrate these economic benefits in a way which will reduce poverty long-term. It characterizes an approach rather than a product or sector. It relates to ‘sustainable’ tourism, and they have areas in common, but pro-poor tourism is different, with a higher focus upon poverty. Many African countries are characterized by high levels of poverty, and there is a consequent need for strategy to incorporate pro-poor measures into tourism (Ashley et al 2001). Pro-poor tourism also helps the tourist feel involved with the people of the region visited (Okech 2010). Pro-poor tourism is a multi faceted approach which includes, for example, offering support to small local businesses, boosting tourism to rural areas, forming partnerships between local communities and businesses, involving communities in planning and improving tourism in ways which clearly benefit the poor (for example improving working conditions) (Kandari and Chandra 2004).Other strategies can include promoting the ability of local people to provide tourist products, marketing, linking with private sector, policy and participative decision-making. A pro-poor initiative can focus upon the small scale or take the form of a national scheme. The various aspects of pro-poor strategy can be analysed into three streams. First, the aim to expand economic benefits for people in poverty, second to deal with the non-economic consequences of poverty, and third to develop core policies, systems and partnerships. Evidence so far suggests that pro-poor tourism initiatives can help lift people out of poverty, although success seems to depend to some extent upon access to education and infrastructure, and results are further mediated by cultural factors. The accessibility of regions (including not just locations but the existence of cultural elites, social constraints), the commercial viability of the product and national and local policies all play a part in determining success. Overall, pro-poor tourism (PPT) works best in the context of a wider agenda for the area and already well developed areas. There is also a need for a ‘stakeholder’ approach in which all interested parties have a say. Although a new development, there are signs of infrastructure to address the demand for pro-poor tourism, for example the African Pro-Poor Tourism Development Centre in Kenya (Okech 2010)

Other factors in the African situation include technology and infrastructure. While mobile services are growing quickly, and mobile phones becoming widely used, Africa’s online provision lacks behind the rest of the world with only 6.2% of the population having internet access (this varies between countries) (Euromonitor 2010). This lack of connectivity in Africa and a poor digital infrastructure will have clear impacts upon tourism in Africa, for example on the ability of small-scale businesses to promote their services, on the awareness of local people of employment opportunities, and of the more widespread marketing of African destinations as a whole to overseas tourists.

Problems with infrastructure are not limited to online and digital services. Hotel provision and road, rail and airport networks are underdeveloped.Most current visitors to Africa stay in hotels, but hostels, lodges and private accommodation are also used. Independent hotels are dominant, with international chains having presence only in key tourism areas (Euromonitor 2010).Roads need improvement, rail travel is difficult as the network is not comprehensive, services are slow and trains unreliable. Air, after road, is the second most popular transport form, but air travel is expensive and standards questionable. National carriers tend to have a monopoly, and there are few budget air travel providers (Euromonitor 2010)

There has been some recent investment in infrastructure, largely as a result of overseas investment from China in particular. Although not done for the tourist industry directly, the improvements do help the industry considerably, for example the building of the Mkapa Bridge across Tanzania’s Rufiji river has improved access to the southern coast (Nelson 2007).

4.1.4 Further analyses of Competitive Position

Porter’s ‘Five Forces’ model can be used to explore the competitive position of Africa in regards to tourism. Porter isolates five areas which together determine a strategic position for an organisation or enterprise. In terms of the first, the ‘suppliers’ are the African countries which make up Africa as a whole, and within these the myriad of individual suppliers of accommodation, transport and other tourist products. These are primarily small and local providers, but there is scope for expansion here. International suppliers are currently few. In terms of ‘competitive rivalry’, Africa is competing with other tourist destinations, but perhaps more particularly with destinations which have been overlooked in the past, and ones which offer a range of natural attractions. Perhaps the biggest rivals are from the more developed African destinations of North and South Africa, which are better known, better marketed, and more able to cope with tourism due to an established network of hotels and other resources. The threat of substitutes concerns the market’s willingness to accept another offering which addresses the same needs. In an area like tourism, where destinations are the product rather than, for example, soap powder, where a number of products do the same job, there is a need to highlight the unique destination qualities to ensure that there can be no substitute product.

Buyers for the African tourist product are currently outside the mass market. There are also sub-groups of buyers, including those interested in wildlife and safari holidays. Africa as a whole needs to consider whether they want to move into the mass market, or address smaller niches such as eco or pro-poor tourism.

‘Barriers to entry’ are diverse. They include lack of price and quality competitiveness (Christie and Crompton 2003), poor air transport, lack of facilities, lack of adequate information and poor public perceptions of, (and the existence of), poverty, disease and conflict (Kestler).Public health services are underdeveloped, and travellers are more likely to fear for their safety (Gauci et al 2003), and be deterred by the risk associated with turbulent political situations (Eliat and Einav 2003). Marketing needs careful consideration to mitigate the effect of these barriers (Okech 2010).

The models by Dwyer and Kim (2003) and Crouch and Ritchie (1999) discussed earlier can also be used to get an overview of the actual and potential for tourism in Africa, as summarised in the following table:

Figure 6: Dwyer and Kim / Crouch and Ritchie Models for Africa

Dwyer and Kim (‘Integrated Model’)

Crouch-Ritchie Model

Africa

Natural Resources

Cultural / Heritage ResourcesCore Resources (Climate, Culture, Activities Mix, Special Events, Entertainment etc)Wildlife, natural attractions, unique culture, specialised attractions e.g. Safari. Scope for developmentSupporting Factors and Resources (General Infrastructure, quality of service, accessibility of destination, hospitality)Supporting Factors and Resources (Infrastructure, Accessibility, Hospitality, Enterprise)Infrastructure improving, but room for further improvement. Inter and Intra Africa travel can be improved. Also scope for improvement in hotels, other servicesDestination ManagementDestination ManagementAd hocSituational conditionsDestination Policy, Planning, DevelopmentSome government / other schemes, room for new initiativesCompetitive (micro) environmentUnique product can reduce competition from other sources. Main competition for individual destinations other African destinationsGlobal (macro) environmentPoor image of Africa outside continentDemand ConditionsQualifying and Amplifying DeterminantsDemand for eco tourism

4.2 Strategic Planning for Africa

So far, Africa has failed to fully capitalize on its tourism potential, although efforts have been made over the last 30 years and the role tourism can play in the economy has been noted, particularly since 1990 with more recent attempts to set a sustainable agenda (Kareem 2008). This section will, using models identified earlier, look at the current situation and map out possibilities.

In terms of Butler’s life cycle, Africa overall seem to be at stage two ‘involvement’.There is some division between low and high seasons, with most visitors during October to April, and some attempt to advertise and dedicate resources to visitors. Individual regions in Africa, and within these individual destinations, vary considerably however, with some well-known resorts at a later developmental stage, and with North and South Africa ahead of West and East.In terms of Jain’s ‘Life Cycle Analysis’, the overall position of Africa seems to be either ‘favourable’ (if barriers to entry can be overcome) or ‘tenable’, with maturity stage predominantly ‘growth’ with individual destinations more or less mature. The aims for this grouping are finding a niche, holding that niche, growing and focussing, which seem to characterise the current need of Africa to overcome problems as a destination and develop a ‘joined up’ approach to the market, for example by addressing issues with political stability, infrastructure, information provision and marketing (Naude and Saayman 2003), lack of skills and training, poor standards, and above all the lack of overall strategy (Rogerson 2007).

One way to focus such a strategy is upon eco- and pro-poor tourism, as part of a wider agenda of sustainability. This focus has the added benefit that it is supported by wider organisations for example the WWF and USAID, who have already donated money to help African destinations develop eco products including ‘agritourism’, in which city dwellers try rural life by living on working farms (Euromonitor 2010). Ritchie’s ‘Destination Visioning’ seems an ideal way of developing an overall strategy for Africa, and within Africa for individual regions and countries. Rather than imposing a vision from above, through government decision being forced upon Africa’s people, this strategy involves all stakeholders from the offset. This seems the best way to ensure that all, including the poor, have a say in Africa’s future as a destination. Cooper suggests a ‘destination audit’ and ‘visioning workshops’ to gather the views of all interested parties. Yoon’s (2001) model might be a useful way of synthesizing the diverse views of stakeholders. As discussed above, Yoon classifies stakeholder perspectives into the economic, social, cultural and environmental impact, and uses these to quantify a total impact.This seems to suggest a way for conflicting perspectives, for example the need to protect bio-diversity and the need to build larger hotels, to be compared and an overall impact calculated.

Just as North and South Africa have developed as very distinct tourist destinations with unique attractions, there is considerable potential for West and East Africa to develop their own identity as destinations, with East Africa particularly concerned with sustainability, biodiversity and conservation (Nelson 2007; Mugo 2006). Existing research comparing the two regions is largely concentrated on East Africa, where a high potential for conservation-based tourism is found. Ecological resources are currently a major draw for tourists, and offer further economic potential. Kenya and Tanzania have already started to capitalise on this potential with growth promoted by investment as part of wider economic strategies, poverty reduction strategies and infrastructure Improvement. At the same time, there are many areas which are currently undeveloped as destinations (South Tanzania, Mozambique), including coastal regions. There is currently more emphasis upon inland resources and safaris (Nelson 2007).

4.3 Data Analysis

In order to assess the development of tourism in Africa, data from 6 African countries (three from East and three from West Africa) was analyzed, and the results inform and support the discussion above.The six countries are Uganda, Tanzania and Kenya (East) and Senegal, Ghana and Gambia (West). The data, shown in Appendix 1 (tables 1, 2 and 3), from individual countries confirms a general pattern of growth which is more or less marked by country. Data is shown from 2003 to 2007 for West Africa, and 2005 to 2009 for East, for a number of variables including arrivals, arrivals by region, arrivals by main purpose, mode of transport and expenditure. While full data is given, it is interesting to summarize the data into West and East Africa, and also look at distributions for Africa overall. In calculating grouped data, where data was missing for one year for a country, it was calculated by averaging from other years. Where data was missing for a variable across years, an estimate was used based on averages for remaining countries. Note, in the following, ‘West’ and ‘East’ Africa denote the three countries for which data was examined.

For West Africa, (see table 1, appendix 1) tourist arrivals have increased fairly steadily over the five year period. Here there is some overall increase, but a large count in 2003 was not matched in subsequent years. Here future data and data from previous years would be interesting. Spend has also increased, on average over the 5 year period with a slight tailing off in 2007. In terms of GDP, tourism’s share seems pretty level over the 5 year period, starting at 4.75 and at 4.85 in 2007, so a longer period of study is needed here, or to include data from other destinations. Total hotel room numbers has also increased, as has (overall) visitors from Europe. While this paints a positive picture of tourism growth in West Africa, it would have been ideal to include data from a greater number of destinations to avoid ‘skew’ from one particularly popular or unpopular destination. Within East Africa (see table 2, appendix 1), there are no figures for tourism’s contribution to GDP, and only data for Kenya regarding hotels, so these tables have been omitted. However arrivals and arrivals for the purpose of tourism also show growth, as do spend and arrivals from Europe (here 2008/2009 data was missing for Kenya: 2007 was used). Again, the overall trend is upwards. Africa as a whole can also be examined, for the overlapping period of 2005-2007 (see table 3, appendix 1). Arrivals, arrivals for the purpose of tourism, expenditure, and European arrivals have all increased steadily over the three years.

4.4 Section Summary

This section looks at Africa and tourism in detail, using the variety of methodological models discussed in the previous section. Africa’s potential is currently not being realized, and a strategy embracing pro-poor and eco-tourism is likely to be useful. In addition, descriptive data was examined, showing there has already been an increase in tourist numbers to the continent.

5. Forecasting – Regression

5.1. Introduction

As emphasized in the methodology chapter, regression analysis has been conducted on variables from East and West African countries in order to determine the factors that most affect the average number of individuals visiting African countries for tourism and in order to build a forecasting model that could be used to predict future tourism visits. The tables below show the results of a multiple regression analysis on East and West Africa done separately. The dependent variable utilized here is the average number of tourisms that visit the three countries yearly over a five-year period (2003 – 2007), while the independent variables utilized are total arrivals in East Africa, Total Expenditure in East Africa, and Total number of arrivals from Europe. The same variables have been utilized for the West African calculations. These variables were chosen based on the frameworks depicted in the literature review and the case study analysis that ensued.

As depicted earlier, the total arrivals, total expenditure and number of individuals from Europe, could all positively affect the tourism industry, as they positively contribute to the economy of participating nations. Other variables were also considered, based on inputs from the case study, and these were political stability, incentives for tourism, and infrastructure availability, however on close inspection of the tourism websites of all six countries analyzed, and on review of existing literature, it was found that all these countries were on similar levels in terms of these three major factors. All of them were politically stable with democratic governments. Only Uganda is landlocked, while the others have “attractive beaches”. They all offer incentives for tourism development, and even Tanzania offers reduced visa fees. Finally, they all have relevant infrastructure such as Hotels, and attractions to keep tourists coming back. Due to these factors, no dummy variables could be utilized in conjunction with the regression analysis, so the only variables measured, were those in which we could readily find data on.

5.2. Regression Analysis

Figure 7: Multiple Regression Analysis for East Africa Tourism

Linear Regression

Regression Statistics
R0.97677

R Square0.95408

Adjusted R Square0.81631

Standard Error84.29037

Total Number Of Cases5

TOTAL PURPOSE TOURISM E AFRICA =- 262.5334 + 0.7300 * TOTAL ARRIVALS E AFRICA + 0.1548 * TOTAL EXPEND. E AFRICA – 1.5888 * TOTAL EUROPE ARRIVALS E AFRICA

The multiple regression analysis on East Africa as shown in the table above shows a coefficient of determination of 0.95, thus meaning that over 95% of the tourism growth in East Africa could be explained through the total number of individuals arriving in East Africa, the total expenditure and the total arrivals from Europe.

Similar results were also obtained for West Africa, as the regression analysis, using the same variables yielded a coefficient of determination of 0.837. The results illustrate that up to 84% of the tourism arrivals in West Africa could be explained through total arrivals, total expenditure and total European arrivals.

Figure 8: Multiple Regression Analysis for West Africa Tourism

Linear Regression

Regression Statistics
R0.91538

R Square0.83793

Adjusted R Square0.35171

Standard Error37.52201

Total Number Of Cases5

TOTAL PURPOSE TOURISM W AFRICA =- 16.9176 + 0.2295 * TOTAL ARRIVALS W AFRICA – 0.5357 * TOTAL EXPEND. W AFRICA + 1.0500 * TOTAL EUROPE ARRIVALS W AFRICA

Though the data utilized in the regression analysis were based on the three topmost countries in both regions (based on UN World Tourism Organisation ranking), the researcher believes that they offer a fair explanation of the determinants of tourism growth within the individual countries, and could thus be utilized in developing a framework for forecasting tourism growth within individual countries. Based on the regression analysis for East Africa, with a coefficient of determination of over 95%, the following formula could be used for predicting future tourism growth:

T = -262.53 + 0.73 (TA-EA) + 0.1546 (TE-EA) – 1.5888 (TEA)

Where T = Total visits for tourism

TA-EA = Total arrivals in the East African country

TE-EA = Total expenditure in the East African country

TEA = Total European Arrivals

The same holds through for West Africa, with 83.7% predication rate. The formula for predicting future tourism growth in West Africa would be:

T = -16.92 + 0.2295 (TA-WA) – 0.5357 (TE-WA) + 1.05 (TEA)

Where T = Total visits for tourism

TA-WA = Total arrivals in the West African country

TE-WA = Total expenditure in the West African country

TEA = Total European Arrivals

The results from the regression analysis show that countries in West and East Africa that are seeking to improve their tourism industry should do so by making efforts to increase general total arrivals within their countries, by making their countries more attractive to foreign visitors. They should also improve expenditure within their countries, whilst attracting European visitors for holidays. The results also confirm that of Kareem (2008), who found that tourism and economic growth are significantly related.

5.3. 10 Year Forecast

10-year forecasts were made for the East and West African tourism countries, in a bid to forecast how the industry would generally perform in coming years. The independent variables were utilized in forecasting the total number of tourism visitors for East Africa, based on the Regression analyses present in Figures 7 and 8. The forecast figures for the independent variables were calculated using Compounded Annual Growth Rates (CAGR) over the past five years, to predict their growth over the next 10 years.

For East Africa, it was found that Total Arrivals in East Africa grew by 3.37% from 2003 – 2007; Total expenditures grew by 6.32% and Total European Arrivals grew by 2.11% over the same period.

For West Africa, the calculation showed that Total Arrivals in West Africa grew by 6.6%; Total expenditure grew by 10.22%, while Total European Arrivals grew by only 2.78% over the same period. The regression analysis for West Africa shows a negative relationship between total expenditure and total tourism visits.

East African forecasting figures, as shown in Figure 9, show that the Total Tourism Visits to East Africa is forecasted to grow over the 10 year forecast period from 2007 – 2017, representing a CAGR of 4.3%.

Figure 9: Forecast for Tourism Visits in East Africa

The results for West Africa however show a different forecast, as the total tourism visits seem to be inversely related to total expenditure. This is an anomaly, and in sharp contrast to results from East Africa. If it were possible to have much more data over a broader period in time, then this forecast could have been verified. The total tourism visits in West Africa shows a CAGR from 2008 – 2014 of -24.56%, when then results in a negative value.

Figure 10: Forecast for Tourism Visits in West Africa

The results of the regression analysis show a flaw in the data gathering, which is that data from the United Nations World Tourism Organization (UNWTO) only spanned 5 years from 2003 – 2007, so the calculations and forecasts in this study are limited to the Tourism industry within that period. As a result of this limitation, an effective “What If” analysis could not be effectively carried out, as the P value was significantly higher than 0.05 for both multiple regression analysis of East and West Africa.

CHAPTER SUMMARY

Results from the regression analysis show that both East and West African Tourism visits are determined highly through the total number of visits, total European visits and Total expenditure within individual countries. Forecasts for East Africa show that it is predicted to grow by a CAGR of 4.3% over the following ten years from 2008 – 2017; while that for West Africa shows that it is predicted to decline by 24.56% yearly from 2008 – 2014. The decline in West Africa growth is due to a calculated negative correlation between Tourism Growth and Total Expenditure (which grows by over 10% yearly). The results from the regression analysis should be expanded and recalculated with the use of data from a longer period, and not just from 2003 – 2007.

6. Discussion

The case study has pointed out that Africa’s tourism potential is under-developed. Theoretical models, and one drawn from business, for example PESTEL and STEEP allow a clear view of Africa’s current situation regarding tourism, particularly in separating out the different strands which go to make up the overall position. In this analysis it became clear that political and economic factors are particularly important. Africa needs to overcome not merely adverse political and economic conditions (war, poverty, intra-country conflict) but to develop a better public face in the developed world in order to attract tourists. On the other hand, it has become clear at a government level for many African countries that tourism can play a central role in boosting GDP and helping reduce poverty. In consequence, many countries are now taking a pro-active role to incorporate strategies to encourage tourism by promoting it to communities and offering incentives. In addition, the case study reveals that Africa faces other challenges including lack of transport infrastructure that need to be addressed for tourism to play a larger role, and also that the lack of online communication is hampering attempts to raise profile with the rest of the world.Porter’s ‘Five Forces’ allowed a way to see African Tourism as a unified enterprise and to analyse competitive and other forces which dictate how it should proceed. In particular there are several barriers to entry which need to be addressed before progress can be made.

The literature review and case study also looked at differences between East and West Africa. Here, the lack of available research means that mapping out differences in approach to tourism in these areas has been limited. However, in East Africa, it was shown, strategy is frequently based around sustainable development and the need for conservation and retaining biodiversity. The literature review on West Africa also suggests the importance of sustainability, as well as highlighting the significance of economical considerations. There is also an emphasis upon liberalisation and openness. Results from both regions therefore seem to highlight a need to incorporate sustainability into any long-term plan for tourism.

The literature review and case study have shown that there has been relatively little investigation of the economic present and future impact of tourism to Africa so far. While this means the current study provides a welcome addition to the literature, and can point out areas for future development, it also means that there is only limited scope for fully examining what a strategic plan for Africa’s tourism would involve and comparing the results obtained with previous work. In particular, the lack of panel data analysis means that results from this study cannot be usefully compared with other studies in detail. Existing studies seem to have looked at tourism in developed countries, and developing ones like Africa have been neglected. The focus in such studies which do exist is upon the influence of the exchange rate and income on tourism. There have been no studies looking at the role played by sustainable tourism for example.This clearly points to a need for further investigations to flesh out available data.

However, understanding Africa’s position in terms of internal strengths and weaknesses and in terms of micro and macro external forces is insufficient. Indeed, so far the lack of research has contributed to the lack of guidance and policy for African tourism (Christie and Crompton 2001). There is therefore a need to develop a strategic vision which can guide the development of tourism within the region as a whole, in order that East and West Africa can catch up with North and South, who currently dominate as tourist destinations. There is a clear need for Africa to develop a unified vision for tourism. It has been shown above that while Africa has a wealth of natural and cultural resources, and while these resources are currently underdeveloped, it has some way to go before these resources are part of a developed tourism agenda. In these terms, the most useful tool for developing a vision is the notion of ‘destination visioning’. It is clear from the above that such a vision may stand the best chance of success if it embraces ideas of pro-poor and eco-tourism as well as sustainability. Africa’s attractions, particularly those in East Africa, are dominated by natural resources, and there is a need to learn from the case of mass tourism in, for example, coastal Spain to ensure that developments preserve these attractions and that infrastructure is sensitive to nature. In addition, given Africa’s high levels of poverty, that ‘pro-poor’ tourism should be integrated into the plan. This provides a way in which economic and other benefits of tourism can be fed back into the local economy and support the poorest locals. In addition, ‘pro-poor’ tourism is a marketable concept which is of particular interest to a sustainability-conscious market sector. A further clear need is for planning to incorporate stakeholder perspectives. There is a need not for profit to feed back to large scale overseas owners but for planning to be jointly owned, from the onset, by integrating the views of all interested parties including local people, small business owners, local government and others.

Key descriptive data also show a positive increase in tourism and tourism related products in both East and West Africa over recent 5 year periods. This suggests that the process of developing Africa as a tourist destination is already underway, but the data considered also raises questions about longer-term perspectives. There is also some issue over the veracity of data from some countries, which is of concern. However, any development which occurs needs to be harnessed in order to avoid over-development of areas and destruction of natural resources. A ‘destination visioning’ plan would seem a useful way of doing this. Furthermore, the regression analysis has shown that West and East African countries would need to develop their infrastructure with the view of attracting more visitors into their countries, especially from Europe and increasing expenditure; all of which would improve tourism visits within their country.

In summary, while Africa, and particularly East and West Africa are underdeveloped in terms of tourism, and consequently have potential to become more popular destinations, the lack of an overall vision and coherent development policy needs to be addressed. By working together as a whole, Africa can continue to capitalize on increasing tourist interest.

7. Conclusion

The study has looked at the African Tourism industry to generate a strategy for development over the next case, based upon the existing strengths and weaknesses of the industry as a whole. A number of business models were used to generate an overall picture of the industry including micro and macro environmental factors, competitive environment and the implications of economic, government and other factors. This picture shows a continent with countries at very different stages of tourism development with different things to offer. Common themes can however be picked out: there exist some difficulties for Africa which act as barriers to developing tourism, for example the widespread existence of poverty, and lack of infrastructure, however the country has great potential as a tourist destination particularly if a sustainable approach is embraced. The study further looked at ways to generate a strategy for development, and it was argued that a model based around the concept of destination visioning is likely to be most successful. This concept allows the views of all stakeholders to be taken into consideration in a process which collects views from all interested parties from the outset of planning. It was also suggested that any future strategy should be firmly rooted around sustainability, eco-awareness and particularly ‘pro-poor’ tourism, as this is likely to lead to a future in which natural and cultural resources are retained, in which the economic benefits of tourism are more equally shared, and in which resources are returned to the poorest members of society.

Data collected from three East African and three West African countries was analysed, and showed that over a recent five-year period there has already been a notable increase in tourism and associated indicators.

There are a number of limitations of this study. The amount of data available is restricted, and what does exist is of questionable validity. While there exists considerable numbers of studies looking at tourism in South and North Africa, other regions have been correspondingly neglected. It has therefore proved more challenging first to create an overall picture and second to extrapolate differences between East and West Africa. In addition, quantitative data was collected only over a five-year period. While this can give insight into the way tourism seems to be increasing, a longer term perspective would give more insight. In addition, if data was available for changing attitudes amongst tourists to Africa as a destination, this would have allowed further useful analyses to be carried out.

The study also suggests some areas for future investigation. For example, it would be useful to take a case study approach to African countries where a long-term tourism strategy is being developed, in order to assess whether this process if feasible and whether any problems – for example reconciling very different stakeholder perspectives – can be overcome. Similarly, it would be useful to assess the impact of pro-poor and other sustainability initiatives and their impact upon tourism in Africa.

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APPENDIX

Appendix 1: West African Data

2003

2004

2005

2006

2007

Arrivals total (000) Gambia

460

613

487

643

550.75

Senegal

502

677

779

876

879

Ghana

584

429

497

587

698

TOTAL ARRIVALS W AFRICA

1546

1719

1763

2106

2127.75

2003

2004

2005

2006

2007

Arrivals Purpose Tourism Gambia

93

111

126

122

81

Senegal

139

97

112.5

114

107

Ghana

185

83

99

106

133

TOTAL PURPOSE TOURISM W AFRICA

417

291

337.5

342

321

2003

2004

2005

2006

2007

Expenditure Gambia

59

69

100

81

64

Senegal

269

287

334

329

304.75

Ghana

495

867

910

990

970

TOTAL EXPEND. W AFRICA

823

1223

1344

1400

1338.75

2003

2004

2005

2006

2007

Indicators Share Tourism GDP Gambia

4.75

5.85

5.5

5.05

4.85

Senegal

3.9

3.6

3.8

3.5

3.7

Ghana

5.6

8.1

7.2

6.6

6

AVERAGE SHARE TOURISM W AFRICA

4.75

5.85

5.5

5.05

4.85

2003

2004

2005

2006

2007

Hotels number rooms Gambia

14809

15426.5

19338.5

18315

20126

Senegal

11539

12101

15842

15842

15842

Ghana

18079

18752

22835

20788

24410

TOTAL HOTEL ROOMS W AFRICA

44427

46279.5

58015.5

54945

60378

2003

2004

2005

2006

2007

Arrivals from Europe Gambia

93

101

121

123

115

Senegal

252

349

393

324

324

Ghana

145

101

123

123

123

TOTAL EUROPE ARRIVALS W AFRICA

490

551

637

570

562

Appendix 2: East African Data

2005

2006

2007

2008

2009

Arrivals total (000)
Uganda468

539

642

844

817

Tanzania613

644

719

770

714

Kenya1479

1601

1817

1203

1490

TOTAL ARRIVALS E AFRICA2560

2784

3178

2817

3021

2003

2004

2005

2006

2007

Arrivals Purpose Tourism
Uganda9

30

140

144

128

Tanzania467

495

580

650

733

Kenya1063

1088

1279

936

1061

TOTAL PURPOSE TOURISM E AFRICA1539

1613

1999

1730

1922

2003

2004

2005

2006

2007

Expenditure
Uganda382

347

402

531

683

Tanzania835

986

1215

1293

1192

Kenya969

1181

1514

1398

1095

TOTAL EXPEND. E AFRICA2186

2514

3131

3222

2970

2003

2004

2005

2006

2007

Indicators Share Tourism GDP
Uganda
Tanzania
Kenya
AVERAGE SHARE TOURISM E AFRICA0

0

0

0

0

2003

2004

2005

2006

2007

Hotels number rooms
Uganda
Tanzania
Kenya3302

4501

5044

2080

4062

TOTAL HOTEL ROOMS E AFRICA3302

4501

5044

2080

4062

2003

2004

2005

2006

2007

Arrivals from Europe
Uganda62

71

77

106

80

Tanzania220

229

274

246

233

Kenya
TOTAL EUROPE ARRIVALS E AFRICA282

300

351

352

313

Appendix 3: East and West Africa

2005

2006

2007

TOTAL ARRIVALS E AFRICA2560

2784

3178

TOTAL ARRIVALS w AFRICA1763

2106

2127.5

TOTAL ARRIVALS AFRICA4323

4890

5305.5


TOTAL PURPOSE TOURISM E AFRICA1539

1613

1999

TOTAL PURPOSE TOURISM W AFRICA337.5

342

321

TOTAL PURPOSE TOURISM AFRICA1876.5

1955

2320


TOTAL EXPEND. E AFRICA2186

2514

3131

TOTAL EXPEND. W AFRICA1344

1400

1338.75

TOTAL EXPEND. AFRICA3530

3914

4469.75


TOTAL EUROPE ARRIVALS E AFRICA1405

1384

1588

TOTAL EUROPE ARRIVALS W AFRICA637

570

562

TOTAL EUROPE ARRIVALS AFRICA2042

1954

2150

2005

2006

2007

TOTAL ARRIVALS E AFRICA2560

2784

3178

TOTAL ARRIVALS w AFRICA1763

2106

2127.5

TOTAL ARRIVALS AFRICA4323

4890

5305.5

TOTAL PURPOSE TOURISM E AFRICA1539

1613

1999

TOTAL PURPOSE TOURISM W AFRICA337.5

342

321

TOTAL PURPOSE TOURISM AFRICA1876.5

1955

2320

TOTAL EXPEND. E AFRICA2186

2514

3131

TOTAL EXPEND. W AFRICA1344

1400

1338.75

TOTAL EXPEND. AFRICA3530

3914

4469.75

TOTAL EUROPE ARRIVALS E AFRICA1405

1384

1588

TOTAL EUROPE ARRIVALS W AFRICA637

570

562

TOTAL EUROPE ARRIVALS AFRICA2042

1954

2150

Appendix 4: Regression analysis of West Africa

Linear Regression

Regression Statistics
R0.91538

R Square0.83793

Adjusted R Square0.35171

Standard Error37.52201

Total Number Of Cases5

TOTAL PURPOSE TOURISM W AFRICA =- 16.9176 + 0.2295 * TOTAL ARRIVALS W AFRICA – 0.5357 * TOTAL EXPEND. W AFRICA + 1.0500 * TOTAL EUROPE ARRIVALS W AFRICA

ANOVA

d.f.

SS

MS

F

p-level

Regression3.

7,278.89885

2,426.29962

1.72335

0.49838

Residual1.

1,407.90115

1,407.90115

Total4.

8,686.8

Coefficients

Standard Error

LCL

UCL

t Stat

p-levelH0 (5%) rejected?
Intercept

-16.9176

446.92074

-5,695.58406

5,661.74885

-0.03785

0.97591

No
TOTAL ARRIVALS W AFRICA

0.22949

0.1841

-2.10973

2.56871

1.24656

0.43041

No
TOTAL EXPEND. W AFRICA

-0.53566

0.31258

-4.50731

3.43598

-1.7137

0.33628

No
TOTAL EUROPE ARRIVALS W AFRICA

1.05001

0.9154

-10.58131

12.68133

1.14704

0.45647

No
T (5%)12.7062

LCL – Lower value of a reliable interval (LCL)
UCL – Upper value of a reliable interval (UCL)

Residuals
Observation

Predicted Y

Residual

Standard Residuals

1

411.53219

5.46781

0.29145

2

301.02018

-10.02018

-0.5341

3

336.60354

0.89646

0.04778

4

314.97171

27.02829

1.44066

5

344.37238

-23.37238

-1.2458

Appendix 5: Regression Analysis of East Africa

Linear Regression

Regression Statistics
R0.97677

R Square0.95408

Adjusted R Square0.81631

Standard Error84.29037

Total Number Of Cases5

TOTAL PURPOSE TOURISM E AFRICA =- 262.5334 + 0.7300 * TOTAL ARRIVALS E AFRICA + 0.1548 * TOTAL EXPEND. E AFRICA – 1.5888 * TOTAL EUROPE ARRIVALS E AFRICA

ANOVA

d.f.

SS

MS

F

p-level

Regression3.

147,608.3332

49,202.77773

6.92522

0.27075

Residual1.

7,104.8668

7,104.8668

Total4.

154,713.2

Coefficients

Standard Error

LCL

UCL

t Stat

p-levelH0 (5%) rejected?
Intercept

-262.53336

828.02569

-10,783.59733

10,258.53062

-0.31706

0.80454

No
TOTAL ARRIVALS E AFRICA

0.73003

0.282

-2.85309

4.31314

2.58878

0.23467

No
TOTAL EXPEND. E AFRICA

0.15484

0.32694

-3.99936

4.30904

0.4736

0.71842

No
TOTAL EUROPE ARRIVALS E AFRICA

-1.58876

4.08199

-53.45542

50.27789

-0.38921

0.7637

No
T (5%)12.7062

LCL – Lower value of a reliable interval (LCL)
UCL – Upper value of a reliable interval (UCL)

Residuals
Observation

Predicted Y

Residual

Standard Residuals

1

1,496.78518

42.21482

1.00165

2

1,682.50098

-69.50098

-1.64908

3

1,984.64089

14.35911

0.34071

4

1,733.60255

-3.60255

-0.08548

5

1,905.4704

16.5296

0.39221

Appendix 6: Tourism Growth Forecast in West Africa

TOTAL PURPOSE TOURISM W AFRICA

TOTAL ARRIVALS W AFRICA

TOTAL EXPEND. W AFRICA

TOTAL EUROPE ARRIVALS W AFRICA

2003

417

1546

823

490

2004

291

1719

1223

551

2005

337.5

1763

1344

637

2006

342

2106

1400

570

2007

321

2127.75

1338.75

562

2008

319.65

2268.10

1475.57

577.62

2009

290.06

2417.71

1626.37

593.68

2010

254.95

2577.19

1792.58

610.18

2011

213.64

2747.19

1975.78

627.15

2012

165.36

2928.41

2177.71

644.58

2013

109.28

3121.58

2400.26

662.50

2014

44.47

3327.49

2645.57

680.91

2015

-30.12

3546.98

2915.94

699.84

2016

-115.64

3780.95

3213.95

719.30

2017

-213.37

4030.35

3542.41

739.29

1.0660

1.1022

1.0278

-24.56%

6.60%

10.22%

2.78%

Appendix 7: Tourism Growth Forecast in East Africa

TOTAL PURPOSE TOURISM E AFRICA

TOTAL ARRIVALS E AFRICA

TOTAL EXPEND. E AFRICA

TOTAL EUROPE ARRIVALS E AFRICA

2003

1539

2560

2186

282

2004

1613

2784

2514

300

2005

1999

3178

3131

351

2006

1730

2817

3222

352

2007

1922

3021

2970

313

2008

1997.47

3122.72

3157.75

319.60

2009

2094.38

3227.86

3357.37

326.33

2010

2195.60

3336.55

3569.61

333.21

2011

2301.34

3448.89

3795.26

340.24

2012

2411.81

3565.02

4035.18

347.41

2013

2527.24

3685.05

4290.26

354.73

2014

2647.86

3809.13

4561.47

362.21

2015

2773.94

3937.39

4849.83

369.84

2016

2905.73

4069.96

5156.41

377.64

2017

3043.52

4207.00

5482.38

385.60

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